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Showing 3 results for Artificial Neural Network

M. H. Shojaeefard, M. M. Etghani, M. Tahani, M. Akbari,
Volume 2, Issue 4 (10-2012)

In this study the performance and emissions characteristics of a heavy-duty, direct injection, Compression ignition (CI) engine which is specialized in agriculture, have been investigated experimentally. For this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (BSFC), peak pressure (PP), nitrogen oxides (NOx), carbon dioxide (CO2), Carbon monoxide (CO), hydrocarbon (HC) and Soot emissions has been considered. The tests were performed at various injection timings, loads and speeds. It is used artificial neural network (ANN) for predicting and modeling the engine performance and emission. Multi-objective optimization with respect to engine emissions level and engine power was used in order to deter mine the optimum load, speed and injection timing. For this goal, a fast and elitist non-dominated sorting genetic algorithm II (NSGA II) was applied to obtain maximum engine power with minimum total exhaust emissions as a two objective functions.

Mr Mostafa Pahlavani, Dr Javad Marzbanrad,
Volume 11, Issue 1 (3-2021)

In the present work, the energy absorption study of warm-rolled LZ71 sheet is done for the first time. To do so, Lithium (7% Wt), Zinc (1% Wt) and Magnesium are cast in 770⁰C. After that, the billet has been warm-rolled at 350⁰C and its thickness reduced by 80%. Then, two different heat treatment situations are studied to reach an isotropic plate. Afterward, microstructures of the specimens have been studied using an optical microscope. Tensile tests of the samples are derived to study the mechanical properties and isotropy of the sheets. Moreover, the results of tensile tests applied for crushing simulations. Energy absorption study of the alloy is also done using ABAQUS/Explicit commercial code. The results of simulations are validated using experimental tests of A6082 and completely acceptable performance of simulations is observed. Then, the mechanical properties of LZ71 are used to study the crashworthiness behavior of the mentioned alloy. Crash absorption parameters, namely peak crush force (FMax), mean crush force (FMean), Total Energy Absorption (TAE), Crush Force Efficiency (CFE), Specific Energy Absorption (SEA) and Total Efficiency (TE) of LZ71 and A6082 are compared which are shown that the performance of LZ71 is considerably more efficient than A6082. Lastly, by the help of Artificial Neural Network (ANN) and Taguchi Method, the effects of dimensional parameters of tube, namely diameter, length and thickness, on FMax, FMean and TAE and also the influences of dimensionless geometrical ratios, namely L/D and D/t on CFE, SEA and TE are surveyed comprehensively.

Behzad Samani, Dr Amir Hossein Shamekhi,
Volume 11, Issue 1 (3-2021)

In this paper, an adaptive cruise control system is designed that is controlled by a neural network model. This neural network model is trained with data resulting from the simulation of a multi-objective nonlinear predictive adaptive cruise control system. For this purpose, first, an adaptive cruise control system was designed using the concept of model predictive control based on a nonlinear model to maintain the desired speed of the driver, maintain a safe distance with the car in front, reducing fuel consumption and increasing ride comfort. Due to the time-consuming computations in predictive control systems and the consequent need for powerful and expensive hardware, it was decided to use the extracted data from the simulation of this designed cruise control system to train a neural network model and use this model to achieve control objectives instead of the predictive controller. Using the neural network model in the cruise control system, despite a significant reduction in computation time, the control objectives were well achieved, and in fact a combination of model predictive controller accuracy and neural network controller speed was used.

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